Data Engineer

What do you drive for us?

● 2-10 years of experience as a Data Engineer or similar role:

Proven track record of designing and implementing data solutions on Google Cloud

Platform.

As a Data Engineer at Randstad, you will play a pivotal role in designing, implementing, and

maintaining our data infrastructure on Google Cloud Platform. You will work closely with our business

intelligence, engineering, and business teams to ensure that our data is collected, stored, processed,

and analyzed effectively, enabling us to make informed decisions and drive impactful outcomes.

What a typical day at work would look like?

● Collaborate with stakeholders: Engage in discussions with business users, BI

analysts, data engineers to understand their data needs and translate them into

technical solutions.

● Design and implement data pipelines: Architect and build data pipelines using GCP

services like Cloud Dataflow, Cloud Dataproc, and BigQuery to ensure efficient data

ingestion, transformation, and delivery.

● Develop data models: Design and implement data models in BigQuery, considering

performance, scalability, and security requirements.

● Manage and optimize data storage: Manage data storage on Google Cloud Storage,

optimizing for cost-efficiency and performance.

● Ensure data quality: Implement data validation and quality checks throughout the data

pipeline to ensure data accuracy and integrity.

● Troubleshoot and resolve issues: Diagnose and solve complex data-related issues, ensuring

smooth data flow and optimal performance.

● Stay updated on GCP advancements: Keep abreast of the latest Google Cloud Platform

offerings and best practices to continually enhance our data solutions.

Your Key Knowledge Areas shall be:

● Deep understanding of GCP data services: Expertise in using GCP services such as Cloud

Dataflow, Cloud Dataproc, BigQuery, Cloud Storage, Data Catalog, and Data Fusion. ● Data

modeling and architecture: Proven experience designing and implementing data models,

including dimensional modeling and data warehousing principles.

● Data warehousing and data lakes: Strong understanding of data warehousing concepts, data

lake architectures, and best practices for data management.

● Data pipeline development: Proficiency in building data pipelines using tools like Apache

Beam, Apache Spark, or other relevant technologies.

● SQL and Python programming: Strong command of SQL for data querying and manipulation,

and proficiency in Python for data analysis and automation.

● Data governance and security: Understanding of data governance principles, data security

best practices, and compliance requirements.